双基地雷达系统的非线性优化定位算法

Cheng Hongwei, S. Zhongkang
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引用次数: 6

摘要

本文讨论了一种双基地雷达系统的非线性优化定位算法。本文提出的双基地雷达系统包括一个T/R站和一个R站。通常T/R站提供方位角和距离等数据,在某些情况下提供目标的仰角,而R站则测量目标的方位角和距离之和。由于双基地系统的基线较长,坐标变换是不可缺少的。因此,提出了五种测量方法的转换,以获得位置分辨率。从理论上讲,目标的三维定位只需要三个独立的测量值,对应于三个独立的位置面,因此可以利用这种数据冗余来提高定位精度。一些论文从测量子集划分、测量子集的GDOP分析、简化LMS估计(SLMS)或选择最优子集(SBS)等方面讨论了定位方法。SLMS方法是基于假设每两个不同子集之间的相关性是弱相关的,可以忽略。事实上,并非如此。数据冗余在SBS方法中没有得到充分利用。本文提出了一种非线性优化方法,该方法假设T/R-R系统的GDOP在观测空间的变化是光滑的,可以利用非线性LMS学习方法逼近控制观测区域的LMS估计的加权矩阵。给出了不同方法的蒙特卡罗仿真测试结果,表明了定位精度的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A nonlinear optimized location algorithm for bistatic radar system
This paper discusses a nonlinear optimized location algorithm for bistatic radar system. The bistatic radar system proposed in the paper contains a T/R station and an R station. Generally, the T/R station provides data such as azimuth angle and distance, and in some circumstance, elevation of a target while the R station has measurements of azimuth angle and distance sum of the target. The coordinate transform is indispensable because of the long base line of a bistatic system. Thus five measurements transformed are presented for location resolution. Theoretically, 3-D location of a target requires only three independent measurements which correspond to three independent surfaces of position, therefore, it is possible to use this data redundancy to improve location accuracy. Some papers have discussed the location methods by means of measurement subsets division, GDOP analysis of measurement subsets, simplified LMS estimation (SLMS) or selecting the best subset (SBS) for solution. The SLMS method is based on the assumption that the relevance between every two different subsets is weak correlated to be neglected. In fact, it is not so. Data redundancy has not been sufficiently used in the SBS method. A nonlinear optimized method is presented in this paper which is based on the assumption that the change of GDOP in observation space of a T/R-R system is smooth that the weighted matrix of LMS estimation in the controlled observation area can be approached by means of nonlinear LMS learning method. Monte Carlo simulation test results of different methods are also given to show the improvement in location precision.
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